real AI and ML Insights

Most of our customers do not have the scientists, technologists or processing capacity—not to mention the time—to take on complex R&D projects that can have massive impacts on their business strategies. Our goal is to take on this lofty task and share our findings with our customers through a series of white papers.

The knowledge gained through R&D helps our team stay on the bleeding-edge of this ever-changing space, and ultimately create differentiated capabilities in the AI and ML space. Through our Advanced Technology Center, WWT data scientists can develop and demonstrate these new and exciting ideas.

A new kind of ai white paper

Most of the thought leadership and white papers produced in the AI and ML fields are produced in academic settings. The results may have applicability to industry, but they’re not driven by industry needs. While informative, they’re not being proven out in the real world. WWT’s data scientists come from these academic and research-focused organizations, but our test ground is industry.

We have the privilege of experimenting with some of the most fascinating data sets and technologies because we’re working and partnering with the biggest organizations in the world, and they are betting on us to open new lines of business through AI and ML.

We are excited to embark on this R&D effort and share some of our most insightful findings real-time. The white papers will be deeply technical, but all grounded in practical challenges we are seeing with our customers.

Additional Resources

Leverage WWT's ServiceNow Automation & Orchestration expertise to accelerate your company's ServiceNow journey. Our ITSM practice and Services Catalog of more than 50 no-touch automations can help you get the most out of this exciting technology.

We have highlighted some practical considerations for the Deep Learning practitioner relevant to neural network training on the NVIDIA DGX-1. Benchmarking experiments showed that GPU performance is related to three dimensions.

Current computer vision-based methods for identifying broken teeth on mining shovels suffer from a prohibitively high false positive rate (FPR). We describe a two-stage methodology for the detection of broken teeth that reduces the FPR.